Package: globpso 1.3.0

globpso: Swarm Intelligence Optimization

A fast and flexible general-purpose implementation of Particle Swarm Optimization (PSO) and Differential Evolution (DE) for solving global minimization problems is provided. It is designed to handle complex optimization tasks with nonlinear, non-differentiable, and multi-modal objective functions defined by users. There are five types of PSO variants: Particle Swarm Optimization (PSO, Eberhart & Kennedy, 1995) <doi:10.1109/MHS.1995.494215>, Quantum-behaved particle Swarm Optimization (QPSO, Sun et al., 2004) <doi:10.1109/CEC.2004.1330875>, Locally convergent rotationally invariant particle swarm optimization (LcRiPSO, Bonyadi & Michalewicz, 2014) <doi:10.1007/s11721-014-0095-1>, Competitive Swarm Optimizer (CSO, Cheng & Jin, 2015) <doi:10.1109/TCYB.2014.2322602> and Double exponential particle swarm optimization (DExPSO, Stehlik et al., 2024) <doi:10.1016/j.asoc.2024.111913>. For the DE algorithm, six types in Storn, R. & Price, K. (1997) <doi:10.1023/A:1008202821328> are included: DE/rand/1, DE/rand/2, DE/best/1, DE/best/2, DE/rand_to-best/1 and DE/rand_to-best/2.

Authors:Ping-Yang Chen [aut, cre], Russell Eberhart [ctb], Kennedy Kennedy [ctb], Jun Sun [ctb], Bin Feng [ctb], Wenbo Xu [ctb], Mohammad Reza Bonyadi [ctb], Zbigniew Michalewicz [ctb], Ran Cheng [ctb], Yaochu Jin [ctb], Milan Stehlík [ctb], Weng Kee Wong [ctb], Jozef Kiseľák [ctb], Rainer Storn [ctb], Kenneth Price [ctb], Okazaki Naoaki [cph]